ログイン 新規登録
言語:

WEKO3

  • トップ
  • ランキング


インデックスリンク

インデックスツリー

  • RootNode

メールアドレスを入力してください。

WEKO

One fine body…

WEKO

One fine body…

アイテム

  1. 論文誌(ジャーナル)
  2. Vol.59
  3. No.2

PADetective: A Systematic Approach to Automate Detection of Promotional Attackers in Mobile App Store

https://ipsj.ixsq.nii.ac.jp/records/185893
https://ipsj.ixsq.nii.ac.jp/records/185893
122a96c7-4117-484c-84ca-9715f41d1fdf
名前 / ファイル ライセンス アクション
IPSJ-JNL5902072.pdf IPSJ-JNL5902072.pdf (970.0 kB)
Copyright (c) 2018 by the Information Processing Society of Japan
オープンアクセス
Item type Journal(1)
公開日 2018-02-15
タイトル
タイトル PADetective: A Systematic Approach to Automate Detection of Promotional Attackers in Mobile App Store
タイトル
言語 en
タイトル PADetective: A Systematic Approach to Automate Detection of Promotional Attackers in Mobile App Store
言語
言語 eng
キーワード
主題Scheme Other
主題 [一般論文(推薦論文)] mobile app store, promotional attack, machine learning
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
著者所属
Waseda University
著者所属
The Hong Kong Polytechnic University
著者所属
NTT Secure Platform Laboratories
著者所属
NTT Secure Platform Laboratories
著者所属
Waseda University
著者所属(英)
en
Waseda University
著者所属(英)
en
The Hong Kong Polytechnic University
著者所属(英)
en
NTT Secure Platform Laboratories
著者所属(英)
en
NTT Secure Platform Laboratories
著者所属(英)
en
Waseda University
著者名 Bo, Sun

× Bo, Sun

Bo, Sun

Search repository
Xiapu, Luo

× Xiapu, Luo

Xiapu, Luo

Search repository
Mitsuaki, Akiyama

× Mitsuaki, Akiyama

Mitsuaki, Akiyama

Search repository
Takuya, Watanabe

× Takuya, Watanabe

Takuya, Watanabe

Search repository
Tatsuya, Mori

× Tatsuya, Mori

Tatsuya, Mori

Search repository
著者名(英) Bo, Sun

× Bo, Sun

en Bo, Sun

Search repository
Xiapu, Luo

× Xiapu, Luo

en Xiapu, Luo

Search repository
Mitsuaki, Akiyama

× Mitsuaki, Akiyama

en Mitsuaki, Akiyama

Search repository
Takuya, Watanabe

× Takuya, Watanabe

en Takuya, Watanabe

Search repository
Tatsuya, Mori

× Tatsuya, Mori

en Tatsuya, Mori

Search repository
論文抄録
内容記述タイプ Other
内容記述 Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile device software distribution platforms. When users find an app of interest, they can acquire useful data from the app store to inform their decision regarding whether to install the app. This data includes ratings, reviews, number of installs, and the category of the app. The ratings and reviews are the user-generated content (UGC) that affect the reputation of an app. Therefore, miscreants can leverage such channels to conduct promotional attacks; for example, a miscreant may promote a malicious app by endowing it with a good reputation via fake ratings and reviews to encourage would-be victims to install the app. In this study, we have developed a system called PADetective that detects miscreants who are likely to be conducting promotional attacks. Using a 1723-entry labeled dataset, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied our system to an unlabeled dataset of 57M reviews written by 20M users for 1M apps to characterize the prevalence of threats in the wild. The PADetective system detected 289K reviewers as potential PA attackers. The detected potential PA attackers posted reviews to 136K apps, which included 21K malicious apps. We also report that our system can be used to identify potentially malicious apps that have not been detected by anti-virus checkers.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.26(2018) (online)
DOI http://dx.doi.org/10.2197/ipsjjip.26.212
------------------------------
論文抄録(英)
内容記述タイプ Other
内容記述 Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile device software distribution platforms. When users find an app of interest, they can acquire useful data from the app store to inform their decision regarding whether to install the app. This data includes ratings, reviews, number of installs, and the category of the app. The ratings and reviews are the user-generated content (UGC) that affect the reputation of an app. Therefore, miscreants can leverage such channels to conduct promotional attacks; for example, a miscreant may promote a malicious app by endowing it with a good reputation via fake ratings and reviews to encourage would-be victims to install the app. In this study, we have developed a system called PADetective that detects miscreants who are likely to be conducting promotional attacks. Using a 1723-entry labeled dataset, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied our system to an unlabeled dataset of 57M reviews written by 20M users for 1M apps to characterize the prevalence of threats in the wild. The PADetective system detected 289K reviewers as potential PA attackers. The detected potential PA attackers posted reviews to 136K apps, which included 21K malicious apps. We also report that our system can be used to identify potentially malicious apps that have not been detected by anti-virus checkers.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.26(2018) (online)
DOI http://dx.doi.org/10.2197/ipsjjip.26.212
------------------------------
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AN00116647
書誌情報 情報処理学会論文誌

巻 59, 号 2, 発行日 2018-02-15
ISSN
収録物識別子タイプ ISSN
収録物識別子 1882-7764
戻る
0
views
See details
Views

Versions

Ver.1 2025-01-20 02:49:30.917324
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Cite as

Bo, Sun, Xiapu, Luo, Mitsuaki, Akiyama, Takuya, Watanabe, Tatsuya, Mori, 2018.

Loading...

エクスポート

OAI-PMH
  • OAI-PMH JPCOAR
  • OAI-PMH DublinCore
  • OAI-PMH DDI
Other Formats
  • JSON
  • BIBTEX

Confirm


Powered by WEKO3


Powered by WEKO3